Maximizing Data Rate for Multiway Relay Channels with Pairwise\n Transmission Strategy
Bibliographic record
Abstract
In a multiway relay channel (MWRC), pairwise transmission strategy can be\nused to reduce the computational complexity at the relay and the users without\nsacrificing the data rate, significantly. The performance of such pairwise\nstrategies, however, is affected by the way that the users are paired to\ntransmit. In this paper, we study the effect of pairing on the common rate and\nsum rate of an MWRC with functional-decode-forward (FDF) relaying strategy\nwhere users experience asymmetric channel conditions. To this end, we first\ndevelop a graphical model for an MWRC with pairwise transmission strategy.\nUsing this model, we then find the maximum achievable common rate and sum rate\nas well as the user pairings that achieve these rates. This marks the ultimate\nperformance of FDF relaying in an MWRC setup. Further, we show that the rate\nenhancement achieved through the optimal user pairing becomes less pronounced\nat higher SNRs. Using computer simulations, the performance of the optimal\npairing is compared with those of other proposed pairings in the literature.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".